New feature

This book provides a comprehensive and concrete illustration of time series analysis focusing on the state-space model, which has recently attracted increasing attention in a broad range of fields. The major feature of the book lies in its consistent Bayesian treatment regarding whole combinations of batch and sequential solutions for linear Gaussian and general state-space models: MCMC and Kalman/particle filter. The reader is given insight on flexible modeling in modern time series analysis. The main topics of the book deal with the state-space model, covering extensively, from introductory and exploratory methods to the latest advanced topics such as real-time structural change detection. Additionally, a practical exercise using R/Stan based on real data promotes understanding and enhances the reader’s analytical capability.  

Front Matter Introduction Pages 1-6 Fundamentals of Probability and Statistics Pages 7-21 Fundamentals of Handling Time Series Data with R Pages 23-27 Quick Tour of Time Series Analysis Pages 29-58 State-Space Model Pages 59-68 State Estimation in the State-Space Model Pages 69-87 Batch Solution for Linear Gaussian State-Space Model Pages 89-95 Sequential Solution for Linear Gaussian State-Space Model Pages 97-127 Introduction and Analysis Examples of a Well-Known Component Model in the Linear Gaussian State-Space Model Pages 129-177 Batch Solution for General State-Space Model Pages 179-218 Sequential Solution for General State-Space Model Pages 219-275 Example of Applied Analysis in General State-Space Model Pages 277-301 Back Matter